Configuring Networked Classifiers in Distributed and Resource Constrained Stream Processing Systems

We design algorithms for optimally configuring, in terms of model complexity as well as operating point, a cascade of exclusive classifiers on distributed systems, given underlying resource constraints. Under rate-dependent constraints we design a Viterbi-like strategy to determine the optimal solution with significantly lower computational complexity than an exhaustive search. We illustrate the performance of this strategy on a cascade of classifiers for a speaker verification task, and highlight performance gains over using a single classifier.